Method for calibrating a measuring instrument

ABSTRACT

The invention relates to a method and device for calibrating a measuring instrument with at least two partial systems (&lt;I&gt;K;M&lt;/I&gt;), which can be displaced with regard to one another, and with means for generating an image of at least one first partial system (&lt;I&gt;K&lt;/I&gt;) on at least one detecting component (&lt;I&gt;A&lt;/I&gt;) of at least one partial system. The invention provides that after establishing a mathematical model, a parameter set, which quantifies influencing factors on systematic measurement errors of the measuring instrument and which has at least one parameter, is derived from the mathematical model. Afterwards, the imaging of the structure elements (&lt;I&gt;S&lt;/I&gt;) of a first partial system (&lt;I&gt;K&lt;/I&gt;), said structure elements determining the relative position of a partial system, ensues on a second partial system (&lt;I&gt;M&lt;/I&gt;). The image of the structure elements (&lt;I&gt;S&lt;/I&gt;) of the first partial system (&lt;I&gt;K&lt;/I&gt;) is converted into signals by the detecting component (&lt;I&gt;A&lt;/I&gt;), and at least one signal vector is recorded. Correction values, which reduce systematic measurement errors of the measuring instrument, are derived from the estimated values of the parameter set and made available.

[0001] The invention relates to a method for calibrating a measuring instrument according to the precharacterizing clause of claim 1, and a measuring instrument according to the precharacterizing clause of claim 10 which can be calibrated by this method, a calibration apparatus according to the precharacterizing clause of claim 19 which is suitable for this purpose, a use of the method according to the precharacterizing clause of claim 29, a computer program product according to claim 30 and a computer data signal according to claim 31.

[0002] A primary quality feature of sensors or measuring instruments is the distribution of errors with which the measured values produced by them are associated: typically, it is required that the measurement errors lie with a specified probability within the specified limits or their mean value and their standard deviation lie within specified limits. In the production of measuring instruments, it may be technically or economically advantageous to ignore the accuracy specification and subsequently to determine the systematic measurement errors consciously accepted thereby by means of a suitable method—referred to below as calibration—and to reduce said errors computationally or by adjustments of the measuring instrument to such an extent that the accuracy specifications are fulfilled in subsequent measurements.

[0003] The prior art for the calibration of measuring instruments, for example of angle-measuring instruments of the type described in CH 658 514 A5, consists in measuring, with the still uncalibrated instrument, a number of known measurement positions—referred to below as reference positions—and declaring the difference between measured positions and reference positions as measurement errors at the measured points and interpolating these, by means of a mathematical model describing their dominant components, over the total measuring range of the measuring instrument and processing them numerically and storing them in such a way that they can be computationally compensated in all subsequent measurements, the apparatus correction of the measuring instrument by means of adjusting devices provided for this purpose representing in principle an alternative. A characteristic feature of the calibration of the prior art is that it is based on external means of measurement (for measuring the reference positions).

[0004] The use of external means of measurement for calibrating measuring instruments gives rise to two difficulties, a fundamental one and a technical one. The common cause of both difficulties is the fact that knowledge of the reference positions is also incomplete: incorrect measurements are “corrected” on the basis of other incorrect measurements. This can be effected only by dividing the differences between the two measurements into an instrument error and a reference error. In accordance with the prior art to date, this division is effected on the basis of a statistical estimation procedure which in turn is based on statistical assumptions relating to the correlation of the errors of the two measurements. The credibility and reliability of these assumptions can be established only by further measurements, with the result that a further calibration problem arises. The basically endless cascade of calibration which begins in this way—the fundamental difficulty mentioned—is ended in practice by ensuring that the accuracy of the knowledge of the reference positions is much higher than the accuracy of measurement required by the calibrated measuring instrument. This gives rise to a technical difficulty that a more accurate measurement procedure has to be provided for the reference positions for each measuring instrument to be calibrated, which, for example in the case of angle measurements with accuracies of angular measurement in the sub-angular second range, is technically complicated and hence uneconomical. Moreover, owing to the technical requirements with respect to the reference positions, calibration methods of the prior art are generally carried out by the manufacturer, which makes it more difficult to effect continuous calibration of the measuring instrument for compensation of environmental influences and wear and ageing processes.

[0005] The problems inherent to the prior art to date can be solved only by eliminating their cause, i.e. effecting the calibration without the use of external measuring means.

[0006] This invention relates to such a calibration method—referred to below as self-calibration—for mechanical measuring instruments having at least two partial systems moving relative to one another and generally comprising rigid bodies, as realized, for example, in angle sensors of the type described in CH 658 514 A5, and a measuring instrument provided for carrying out the method, a calibration apparatus, the use of the method for calibrating a plurality of measuring instruments, a computer program product and a computer data signal.

[0007] The technical object of the invention is to provide a method and suitable apparatuses with which a calibration can be carried out without the basic errors originating from comparative measurements. This object is achieved by fundamentally dispensing with external reference positions as self-calibration, but optionally with inclusion of external reference positions as hybrid calibration.

[0008] A further technical object is the possibility of checking the suitability of the self-calibration.

[0009] A further technical object of the invention is the production of a measured value with the calibrated measuring instrument. It is achieved by calculating the measured value as an estimated value by means of a model which describes the measuring procedure and on which the calibration too is based.

[0010] A further technical object of the invention is permanent self-calibration. The achievement according to the invention comprises the inclusion of further parameters (other than only the measured values) in the estimation process, in particular those which quantify environmental effects, such as, for example, temperature influences and ageing processes, on the accuracy of measurement. It would thus be possible to realize permanent self-calibration which can be extended to include multiple measuring instruments and which would possibly stop an environment- or ageing-related deterioration in the accuracy of measurement.

[0011] These objects are achieved, according to the invention, by the characterizing features of claims 1, 10, 19, 29, 30 and 31. Advantageous and alternative embodiments and further developments of the method, of the measuring instrument and of the calibration apparatus are evident from the features of the subclaims.

[0012] In the method, according to the invention, for calibrating a measuring instrument comprising at least two partial systems moving relative to one another and comprising means for producing an image of at least one first partial system on at least one detecting component of at least one partial system, a mathematical model describing the position of the partial systems relative to one another and at least one image is produced in a first step. In principle, all parameters influencing the measuring process, such as, for example, position, shape or structure parameters of the partial systems, and parameters of the image or of the means for producing the image, are used in the modelling. For example, the spatial positions of a light source and of a light-sensitive detector can be used as parameters in the model.

[0013] The partial systems which are movable relative to one another and which are generally rigid bodies but which may also be, for example, fluid or deformable media, are described in this model with respect to those features of their relative position and their physical properties which are relevant for the measuring process. The partial systems may be, for example, movable translationally or rotationally relative to one another, or a liquid surface may have an inclination variable relative to another partial system. For example, a first partial system can be focused onto a liquid surface as a second partial system. From this, focusing is effected in turn onto a third partial system. The focusing onto the third partial system can be described as a function of the position of the liquid surface, for example chosen to be reflective. The degrees of freedom of the relative movement of the partial systems of the measuring instrument are limited by the constraining conditions, such as, for example, a rotation of a partial system relative to the other partial systems about a rigid axis.

[0014] Depending on the design of the measuring instrument, the parameters describing structural elements, such as, for example, the position of individual marks, position parameters of the partial systems and imaging parameters can be linked to one another in the mathematical model. Parameters chosen for formulating the model need not necessarily have a geometric, physical or statistical meaning. Often, it is expedient to convert the original mathematical model into a structurally simpler form by reconfiguration and to dispense with direct interpretability of the new parameters.

[0015] In the next step, an image of structural elements of at least one first partial system, which determine the relative position of the partial system, is focused onto the second partial system so that the image contains information about the positions of the two partial systems relative to one another. The structural elements may represent, for example, the specific external shape of the first partial system or a mark applied to the first partial system. The design of the image of these structural elements must be chosen so that it contains sufficient information for determining the relative position of the partial systems, in particular the size of the section of the structural elements which is required for unambiguous localization of the position being decisive.

[0016] In the following step, the detecting component converts the image of the structural elements of the first partial system into signals from which, in a further step, at least one signal vector having at least one component and containing information about the relative position of the partial systems is recorded. The recording of the signal vector is explained in more detail below.

[0017] In a further step, the “stochastic model errors”, often also referred to as “noise”, i.e. the randomly controlled discrepancy between reality and model, are modelled as random quantities and assumptions are made about their probability distributions, which assumptions in turn may contain unknown parameters. From the at least one signal vector which is linked by the model to the unknown parameters, the parameters are estimated using the statistical estimation theory, so that a quality criterion is optimized. Widely used quality criteria are estimation of maximum likelihood of noise or minimum estimation error variance.

[0018] Statistical parameter estimation methods produce not only estimated values for the model parameters but inevitably also estimated values for the noise, i.e. the residues. According to the model, they are realizations of the random quantities which have been included in the mathematical model. By means of statistical tests, it is now possible to check a posteriori the hypothesis concerning whether they are actually realizations of random quantities with the postulated statistical properties. Such “residue analyses” can give important information about the suitability or worthiness of improvement of the mathematical model used for the calibration.

[0019] In the final step, correction values intended to reduce measurement errors of the measuring instruments are derived from the estimated parameter values and made available. This can be effected by storing the correction values coordinated with a respective position, a computational correction being effected during the measuring process. In principle, when appropriate technical adjusting means are available, the correction values can also be converted into apparatus corrections.

[0020] Individual steps or a plurality of steps of the method can be repeated once or several times. In an embodiment of the method, after creation of the mathematical model for a measuring instrument and derivation of at least one parameter set, the following steps of production of images, conversion thereof into signals and recording of signal vectors are repeated several times in succession. The number of repetitions depends on the intended quality of the estimation of the values of the parameter set, which quality obeys statistical laws.

[0021] After the end of these steps, estimation of the values and derivation and provision of correction values are effected. In another, recursive variant of the method, the values of the parameter set are estimated again after each production of an image and the subsequent steps.

[0022] Another embodiment of the method uses a mathematical model with at least one parameter set, associated therewith, for the calibration processes for a plurality of measuring instruments of the same type, so that the first two steps of the method are carried out only in the calibration of the first measuring instrument of a whole series and the further measuring instruments can be calibrated with the use of this model and of the at least one parameter set.

[0023] In terms of apparatus, the means used for imaging may consist, for example, of at least one electromagnetic radiation source, light in the visible spectral range preferably being used. Owing to the special technical requirements of the measuring instrument to be calibrated, it may in particular be necessary to influence the beam path in the measuring instrument with imaging or wavefront-structuring optical elements or to effect multiple reflection for lengthening the beam path. The image can be reflected back and forth several times between the partial systems, or consecutive imaging of the partial systems on one another can be effected.

[0024] In the production of an image, structural elements of a partial system are focused on a second partial system so that the image contains information about the associated relative position of the partial systems. What is decisive here is that the relative position can be uniquely determined from the image. The imaged component of the first partial system and its structural elements, in particular the density and differentiability thereof, are related. For example, one of the partial systems may be designed in its form so that a sufficiently large image part is sufficient for determining the relative position. This is possible, for example, by a special shape of the contour of the partial system, with position-dependent geometrical parameters; in the case of a disc rotating about an axis, for example, the distance of the disc edge from the axis can be designed as a unique function of the angle with respect to a zero position. In general, however, variation of the shape of a partial system is associated with undesired physical effects, so that alternatively structural elements in the form of a mark may also be applied. This can be effected, for example, by coding with a sequence of alternately transparent and opaque code lines or of code lines having alternately different reflectivity. From a code segment focused on the detecting component, it must then be possible uniquely to determine the relative position of the partial systems.

[0025] In order to avoid undesired physical effects, such as, for example, deviation moments, it is possible, particularly in the case of rotational movements of the partial systems, for one partial system to be formed with a rotationally symmetrical shape, for example as a sphere, cylinder, disc or ring. Coding can then be applied, for example, to a smooth section of the body or in an area in the interior of a translucent body.

[0026] The detecting component and all following means for recording at least one signal vector from the signals of the detecting component, for deriving and making available correction values and for reducing systematic measurement errors of the measuring instrument may contain components of analogue and/or digital electronics and in each case be designed according to the prior art with means for signal and information processing.

[0027] In its technical design, the detecting component is tailored to the requirements specified by the imaging means. In an exemplary use of visible light, it is possible in principle to use all possible forms of light-sensitive sensors, for example photomultipliers, photosensitive diodes or CCD cameras.

[0028] The means for recording at least one signal vector from the signals of the detecting component must meet the technical requirements thereof. For example, they may have an analogue/digital converter (ADC) and at least one processor for processing the signal and for converting them into a signal vector.

[0029] In the means for deriving and making available correction values, the method step comprising the estimation of values of the parameter set from the at least one signal vector is implemented. It is preferably realized by means of at least one electronic computer and supplementary memory modules.

[0030] The means for reducing systematic measurement errors of the measuring instrument may permit purely computational correction of the measured values obtained, for example by an electronic computer, or may comprise apparatuses for mechanical or electronic correction, for example precision mechanical drives, piezoelectric control elements, or an electronic correction of recording errors of the detecting component.

[0031] The method according to the invention and a measuring instrument according to the invention or a calibration apparatus according to the invention are described by way of example for the calibration of an angle-measuring instrument, referred to here as an angle sensor, which is explained in more detail, purely by way of example, on the basis of embodiments shown schematically in the drawing.

[0032]FIG. 1 shows the geometric conditions of the angle sensor described.

[0033] This consists of a sensor housing, shown only partly here, as second partial system M, which is represented below by the light source L, the array A as a detecting component and the axis d of rotation, and a disc freely rotatable relative to this about the axis d of rotation—referred to below as circle—as a first partial system. The light source L as an imaging means forms a segment of a line code Σ arranged radially on the circle and consisting of a sequence of alternately transparent and opaque code lines as structural elements S on the array A of photosensitive diodes, for example a CCD array, as a detecting component in the thrown shadow. The position of a specific structural element on the circle is described by its positional angle α relative to a randomly chosen zero position. The position of the image of a specific structural element S on the array A is described by the image coordinate s. The position of the circle relative to the sensor housing is characterized by the circle position angle βε[0, 2n[, through which the circle has to be rotated from a randomly defined zero position about the axis d of rotation rigidly connected to it, in order to assume its actual position.

[0034] The object of the angle sensor is to form an estimated value {circumflex over (β)} for the circle position angle β from the intensity distribution of the incident light—referred to below as sensor signal—which is scanned by the array A and A/D converted, and to output said estimated value as a result of the measurement which fulfils the specified accuracy requirements for the measurement error {circumflex over (β)}−β.

[0035] There is a need for calibration when the systematic components of the measurement errors make it impossible to comply with the accuracy requirements. Systematic errors are the result of insufficient quantitative knowledge of the influencing factors which, apart from the circle position angle β, contribute to the formation of the sensor signal. Thus, if it is possible to determine more accurately these influencing factors too from sensor signals, in addition to the unknown circle position angles, the angle sensor can achieve the accuracy requirements by self-calibration without external reference angles.

[0036] The features of the angle sensor which contribute substantially to the signal shape must be mentioned explicitly and the mechanisms of their influence on the sensor signal must be revealed. This is effected by means of a mathematical model of the angle sensor which quantitatively links the circle position angle with the sensor signal and in which these influencing factors are used as model parameters, the number of which must be kept finite for practical reasons. Thus, the angle sensor calibration is based on the estimation of the model parameters from sensor signals, i.e. on a classical parameter estimation problem of mathematical statistics. The self-calibration thus differs from the calibration by means of reference angles in that it replaces external measuring means by the internal “optimal” adaptation of a sensor model to the sensor signals. The two calibration methods can be easily combined to give a hybrid calibration.

[0037] The mathematical model of the angle sensor is the foundation of the self-calibration. A method for formulating such a model starts, for example, from the idealized concept that the light source L is a point source, the code lines of Σ are arranged radially in the plane K of the circle, the diodes of the array A are arranged linearly and the circle or the line code Σ is rigidly connected to the axis d of rotation, which ideally—but not necessarily—is perpendicular to the plane K of the circle. The spatial arrangement of L, d, the point of intersection of d with K—referred to below as circle rotation centre D—and A is assumed to be rigid, i.e. invariant as a function of time, and to be designed so that an image of a continuous segment of Σ is produced on A for each circle position angle β.

[0038] The relative positions of L, d, D and A or of Σ, D and d, which are rigid according to the model, can be described by 7 or 4 real parameters according to generally known principles of analytical geometry and with the use of trignometrical functions, and the relative position of these “rigid bodies” is characterized by the circle position angle βε[0,2π[.

[0039] In terms of these 11 position parameters time-invariant according to the model and of the circle position angle β, it is possible to use generally known calculation rules of three-dimensional vector algebra, of elementary algebra and of elementary trigonometry to calculate where a specific structural element S of the line code Σ, for example an edge of a code line which is characterized by the position angle a βε[0,2π[, which it makes with a randomly chosen zero position in K, is produced as an image on the array A. The position of this image on the array A can be described by a dimensionless imaging coordinate sε[−1,1] where −1 denotes one end, 0 the midpoint and +1 the other end of the array A. The zero positions of the position angles α measured in the plane K of the circle and of the circle position angles β measured in the plane normal to d can be matched with one another in such a way that, in the equation which represents the imaging coordinate s as a function of the angles α and β and of the 11 time-invariant position parameters, the circle position angle β occurs only in the difference α-β.

[0040] In addition, this equation can be formally simplified by combining the functional logic operations of the 11 position parameters occurring in it to give new, dimensionless time-invariant model parameters. Finally, a comparison of the individual effects of these new model parameters on s shows which of these effects can be ignored for the purpose of reducing the complexity with a very small model error, and that it is possible to manage with k<11 time-invariant model parameters.

[0041] An expedient choice is k=6, in which case the equation mentioned can be brought into the form $\begin{matrix} \begin{matrix} {s = {\eta \left( {\alpha,{\beta;p},q,u,v,x,y} \right)}} \\ {= \frac{{\sin \quad \left( {\alpha - \beta} \right)} + {{u \cdot \cos}\quad \alpha} + {{v \cdot \sin}\quad \alpha}}{{p \cdot {\cos \left( {\alpha - \beta} \right)}} + {q \cdot {\sin \left( {\alpha - \beta} \right)}} + {{x \cdot \cos}\quad \alpha} + {{y \cdot \sin}\quad \alpha}}} \end{matrix} & (0) \end{matrix}$

[0042] p, q, u, v, x, y denoting the 6 dimensionless time-invariant model parameters and the angles ad α, β,[0,2π[ having the meaning defined above. If the axis d of rotation is perpendicular to the plane K of the circle, x=y=0, and if the circle rotation centre D coincides with the centre of the line code Σ, u=v=0; a further expedient choice is therefore also k=4 and x=y=0. Of fundamental importance—both for the self-calibration and for the angle measurement—is the fact that equation (0) can be uniquely solved for every argument of η in all cases relevant in practice, it being necessary to impose expedient restrictions in the case of solutions for α and β. It is helpful to express the solution of equation (0) for the j th argument of η as a function η_(j) ⁻¹ of all variants involved. Thus, η₁ ⁻¹ (s, β; p, q, u, v, x, y) denotes the unique solution of (0) for the 1st argument α of η in the interval ${{\rbrack\beta} - \frac{\pi}{2}},{\beta + {\frac{\pi}{2}\left\lbrack , \right.}}$

[0043] η₂ ⁻¹(α, β; p, q, u, v, x, y) denotes the unique solution of (0) for the 2nd argument of β of η in the interval ${{\rbrack\alpha} - \frac{\pi}{2}},{\alpha + {\frac{\pi}{2}\left\lbrack , \right.}}$

[0044] etc.

[0045] The solution of equation (0) for the arguments 1 and 3-8 is easily performed using generally known calculation rules of elementary algebra and of elementary trigonometry, and the solution of (0) according to the 2nd argument is $\begin{matrix} {{{{\eta_{2}^{- 1}\left( {\alpha,{s;p},q,u,v,x,y} \right)} = {\alpha - {\arctan \left( \frac{z - {z^{\prime} \cdot \sqrt{1 - z^{\prime 2} + z^{2}}}}{1 - z^{\prime 2}} \right)}}}{z = \frac{p \cdot s}{1 - {q \cdot s}}}\quad {z^{\prime} = \frac{{{\left( {u - {x \cdot s}} \right) \cdot \cos}\quad \alpha} + {{\left( {v - {y \cdot s}} \right) \cdot \sin}\quad \alpha}}{1 - {q \cdot s}}}}\quad} & (1) \end{matrix}$

[0046] if the inequality |z′|<1 is satisfied, which is true in all cases of interest in practice.

[0047] The basis of the angle measurement is the unique solution (1) of the equation (0) for the circle position angle β: if the model parameters p, q, u, v, x and y are known and it is possible to assign the structural element S of the line code Σ, which element is characterized by the position angle α, to its image s on the array A, the circle position angle β can be calculated according to (1). It is therefore necessary for the line code Σ to be decodable, i.e. to arrange the code line in such a way that, for each βε0,2π[ from the image of the code segment projected by the light source L onto the array A, this code segment can be uniquely localized on the circle.

[0048] There are many possibilities for making Σ decodable. A known method uses an m-sequence of length m=2^(λ)−1, where λ is a natural number, i.e. a cyclic binary sequence b, consisting of $\frac{m - 1}{2}$

[0049] zeros and $\frac{m + 1}{2}$

[0050] ones, which has the property that, for each natural number n<m, there is exactly one partial sequence of b consisting of λ successive digits which represents λ in binary form: choose two different angles α⁰,α¹>0 so that ${{{\frac{m - 1}{2} \cdot a^{0}} + {\frac{m + 1}{2} \cdot \alpha^{1}}} = {2\quad \pi}},$

[0051] choose an angle 0<α⁺ ₀<min {α⁰, α¹}, define the angles α_(i) ^(μ)ε[0,2π[ recursively according to α⁻ ₀:=0 and α_(i) ^(μ):=α_(i−1) ^(μ)+^(b(i)) for 0<i<m, and let $\begin{matrix} {{\sum{(\alpha)\text{:}}} = \left\{ {\begin{matrix} {1,{{{{if}\quad \alpha_{i}^{-}} \leq \alpha \leq {\alpha_{i}^{+}\quad {for}\quad {one}\quad i}} \in \left\lbrack {0,{m\lbrack}} \right.}} \\ {0,{otherwise}} \end{matrix},} \right.} & (2) \end{matrix}$

[0052] αε[0, 2π[.

[0053] This line code Σ, where 1 indicates ‘transparent’and 0 indicates ‘opaque’, is a physical realization of the binary sequence b whose sectorial image on A is clearly recognizable with an advantageous choice of λ and the angles α⁰ and α₀ ⁺, which makes Σ decodable.

[0054] The configuration (2) of Σ by the 2m−1 angles α₀ ⁺, α₁ ^(μ), . . . , α_(m−1) ^(μ) is not the most economical one—Σ is completely specified by λ, the principle of formation of the binary sequence b and the angles α⁰ and α⁺ ₀—but is expedient for the calibration. The accuracy of measurement achievable by the angle sensor depends decisively on how accurately the code line positions, i.e. the angles α₀ ⁺,α₁ ^(μ), . . . , α_(m−1) ^(μ) are known. Since precise positioning of the code lines on the circle is complicated, it is advantageous to consider these angles as model parameters to be identified: consequently, the manufacturing tolerances of the circle can be relaxed, and it is merely necessary to ensure that Σ remains decodable, i.e. the binary sequence b is realized. A further advantage of the variability of the angles α₀ ⁺,α₁ ^(μ), . . . , α_(m−1) ^(μ) is that the model error associated with the reduction of the number of position parameters from 11 to k<11 can thus be partly compensated.

[0055] The line code Σ is produced as an image on the array A in the following manner: on the basis of classical optics, the light density I(s) registered by the diode with midpoint sε]−1,1[ is modelled according to $\begin{matrix} {{I(s)} = {\int_{- \infty}^{\infty}{{a\left( {\sigma - s} \right)} \cdot {I^{0}(\sigma)} \cdot {\sum{\left( {\eta_{i}^{- 1}\left( {\sigma,{\beta;p},q,u,v,x,y} \right)} \right)\quad {\sigma}}}}}} & (3) \end{matrix}$

[0056] where I⁰:R→R₊ denotes the continuous intensity distribution of the light incident unhindered on the array A and a:R→R₊ describes both the response behaviour of the diodes of the array A and optical effects, such as blurring, refraction and diffraction generally. The model formulation (3), in particular the translation invariance of the diode response postulated therein, is a simplified idealization which describes the optical imaging only approximately as a statistical average. If ℑ⊂[0, m[ denotes the quantity determined by decoding a certain set of indices of the transparent code line, some or all of which are produced as an image on the array A then $\begin{matrix} {{{I(s)}\overset{3}{\underset{(2)}{\approx}}{\sum\limits_{i \in}^{\quad}\quad {\int_{\eta {({\alpha_{i}^{-},{\beta;p},q,u,v,x,y})}}^{\eta {({\alpha_{i}^{+},{\beta;p},q,u,v,x,y})}}{{{a\left( {s - \sigma} \right)} \cdot {I^{0}(\sigma)}}\quad {\sigma}}}}} = {\sum\limits_{i \in}^{\quad}{{I^{0}\left( \sigma_{i} \right)} \cdot {\int_{s - {\eta {({\alpha_{i}^{+},{\beta;p},q,u,v,x,y})}}}^{s - {\eta {({\alpha_{i}^{-},{\beta;p},q,u,v,x,y})}}}{{a(\sigma)}{\sigma}}}}}} & (4) \end{matrix}$

[0057] The approximation (4) indicates that contributions to the diode response I are neglected if they originate from code lines which are not produced as an image on the array A, and the equation in (4) is derived from the mean value set of the integral calculation for suitable σ_(i)ε[η(α_(i) ⁻, β;p,q,u,v,x,y), η(α_(i) ⁺, β;p,q,u,v,x,y)],i εℑ.

[0058] The function a:R→R₊ which realistically describes the response behaviour of the diodes via (4) can be investigated theoretically or empirically; practical considerations, in particular the required computational effort, suggest an analytical form which is as simple as possible and can be differentiated continuously for all variables and a compact carrier. If, for example, $\begin{matrix} {{{a\left( {{\sigma;t_{-}},t_{+}} \right)}\text{:}} = \left\{ \begin{matrix} {0,} & {t_{+} \leq {\sigma }} \\ {\frac{\left( {t_{+} - {\sigma }} \right)^{2}}{t_{+} \cdot \left( {t_{+} - t_{-}} \right)},} & {{t_{-} < {\sigma } < t_{+}},{{{for}\quad 0} < t_{-} < t_{+}},} \\ {{1 - \frac{\sigma^{2}}{t_{-} \cdot t_{+}}},} & {{\sigma } \leq t_{-}} \end{matrix} \right.} & (5) \end{matrix}$

[0059] then a(·;t⁻,t₊):R→R is a symmetrical quadratic spline with carrier [−t₊,t₊]⊂R, for which the integrals (4) can be easily calculated analytically and are cubic splines in s with compact carriers, which depend only on t_(μ) and η(α_(i) ^(μ), β; p, q, u, v, x, y); iεℑ.

[0060] if the array A of the detecting component consists of n identical diodes, the midpoint of the j th diode has the coordinate ${s_{j} = {\frac{{2j} - 1}{n} - 1}},$

[0061] and (4) suggests modelling the digital response a_(j) ε R₊ of the j th diode according to $\begin{matrix} {{a_{j} = {{\sum\limits_{i \in}^{\quad}{I_{i} \cdot {\int_{s_{j} - {\eta {({\alpha_{j}^{+},{\beta;p},q,u,v,x,y})}}}^{s_{j} - {\eta {({\alpha_{j}^{-},{\beta;p},q,u,v,x,y})}}}{{\alpha (\sigma)}{\sigma}}}}} + w_{j}}},{1 \leq j \leq n}} & (6) \end{matrix}$

[0062] where w_(j) ε R represents all unmodelled effects contributing to the signal formation (such as dark noise, discretization errors, etc.). If the quantities occurring in (6) are combined in the vectors or the matrix $\begin{matrix} {{{a\text{:}} = \begin{bmatrix} \alpha_{j} \\ M \\ \alpha_{n} \end{bmatrix}},{{w\text{:}} = {\begin{bmatrix} w_{1} \\ M \\ w_{n} \end{bmatrix} \in R^{n}}},} \\ {{{A\text{:}} = {{\left\lbrack A_{j\quad i} \right\rbrack \text{:}} = {\left\lbrack {\int_{s_{j} - {\eta {({\alpha_{j}^{+},{\beta;p},q,u,v,x,y})}}}^{s_{j} - {\eta {({\alpha_{j}^{-},{\beta;p},q,u,v,x,y})}}}{{a(\sigma)}{\sigma}}} \right\rbrack \in R^{n \times {}}}}},} \\ {{I\text{:}} = {\begin{bmatrix} \begin{matrix} I_{\min {()}} \\ M \end{matrix} \\ I_{\max {()}} \end{bmatrix} \in R^{}}} \end{matrix}$

[0063] then the following is true for the vector aεR^(n) referred to below as signal vector a  = ( 6 )  A  ( α μ , β ; p , q , u , v , x , y ; t ) · I + w , ( 7 )

[0064] where α_(ℑ) ^(μ):={α_(ℑ) ^(μ)|iεℑ} and t denotes the vector of the parameters which specify a:R→R₊—for example in equation $\begin{matrix} {t = {\begin{bmatrix} t_{-} \\ t_{+} \end{bmatrix} \in {R_{+}^{2}.}}} & (5) \end{matrix}$

[0065] As a final step of the mathematical modelling of the angle sensor, the vector wεR^(n) in (7) is modelled as a random vector whose probability distribution has a density d:R^(n)→R₊. Thus, the angle sensor calibration can be formulated and solved as a statistical parameter estimation problem:

[0066]

With the uncalibrated angle sensor, the signal vectors a¹, . . . ,a^(N)εR^(n) are registered in N circle positions β¹, . . . ,β^(N) which are unknown but distributed as uniformly as possible over the circle.

[0067]

Each signal vector a¹εR^(n) is decoded, i.e. the quantity ℑ^(J)⊂[0,m[ of indices of the transparent code line, some or all of which are produced as an image on the array A, is determined, 1≦J≦N.

[0068]

According to the model, the vectors w J  = ( 7 )  a J - A  ( α J μ , β J ; p , q , u , v , x , y ; t ) · I J ∈ R n , 1 ≤ J ≤ N , ( 8 )

[0069] are independent and distributed identically with probability density d:R^(n)→R₊; the cumulative probability density is thus ∏ J = 1 N     d ( a J - A  ( α J μ , β J ; p , q , u , v , x , y ; t ) · I J ∈ R + . ( 9 )

[0070]

The unknown parameters I¹, . . . , I^(N), β¹, . . . ,β^(N), α₀ ⁺, α₁ ^(μ), . . . ,α_(m−1) ^(μ), p,q,u,v,x,y,t and any further parameters specifying the probability density d are determined so that the probability density (9) has a maximum value, respecting all secondary conditions—for example 0<t⁻<t₊, if (5) is used.

[0071] If a maximum position Î¹, . . . , Î^(N), β¹, . . . β^(N), α̂₀⁺, α̂₁^(μ),  …  α̂_(m − 1)^(μ),

[0072] {circumflex over (p)}, {circumflex over (q)}, û, {circumflex over (v)}, {circumflex over (x)}, ŷ, {circumflex over (t)} of (9) exists it is referred to as maximum likelihood (ML−) value and an algorithm used for calculating it is referred to as maximum likelihood (ML−) estimator for the parameters I¹, . . . ,I¹, β¹, . . . , β^(N), α₀ ⁺, α₁ ^(μ), . . . , α_(m−1) ^(μ), p,q,u,v,x,y,t. ML estimators are proven standard tools of mathematical statistics, and the optimization of multivariable functions is a standard task of numerical mathematics, for the solution of which reliable algorithms—implemented in ready-to-use form in commercially available software packages—are available. An angle sensor calibration carried out according to steps

-

is referred to below as ML calibration.

[0073] If, for illustrating the method, it is assumed that d:R^(N)→R₊ is the density of a normal distribution with expected value {overscore (w)}εR^(n) and (symmetrical and positively defined) covariance matrix CεR^(n×n), then $\begin{matrix} {{{d(w)} = {\frac{\exp \left( \frac{\left( {w - \overset{\_}{w}} \right)^{T} \cdot C^{- 1} \cdot \left( {w - \overset{\_}{w}} \right)}{2} \right)}{\sqrt{\left. {2\pi} \right)^{n} \cdot \det}(C)} = {\frac{\det (G)}{\left( {2 \cdot \pi} \right)^{\frac{n}{2}}} \cdot {\exp \left( {- \frac{{G \cdot \left( {w - \overset{\_}{w}} \right._{2}^{2}}}{2}} \right)}}}},{w \in R^{n}},} & (10) \end{matrix}$

[0074] where G ε R^(n×n) denotes a matrix for which

G ^(T) ·G=C ⁻¹ and det(G)>0  (11)

[0075] —for example, the inverse of the Links-Cholesky factor of the matrix CεR^(n×n) —the ML estimation reduces to the minimization of 1 2  ∑ j = 1 N      G · ( α J - w _ J - A  ( α μ , β J , p , q , u , v , x , y , t ) · I J )  2 2 - N · log  ( det  ( G ) ) . ( 12 )

[0076] If it is even assumed that the measurement errors of the array diodes are signal-independent and statistically independent and have an identical normal distribution, with unknown mean value α₀εR and unknown standard deviation σ<0, this corresponds to the choice ${\overset{\_}{w}\text{:}{= {{\alpha_{0} \cdot I}\quad I_{n}\text{:}{= {{\alpha_{0} \cdot \begin{bmatrix} 1 \\ M \\ 1 \end{bmatrix}} \in {R^{n}\quad {and}\quad G\text{:}{= \frac{1}{\sigma} \cdot {{Diag}\left( {I\quad I_{n}} \right)}}} \in R^{n \times n}}}}}},$

[0077] and (12) furthermore reduces to 1 2 · σ 2 · ∑ J = 1 N   α J - α 0 · I     I n - A  ( α μ , β J ; p , q , u , v , x , y ; t ) · I J )  2 2 + N · n · log  ( σ )

[0078] and hence the ML calibration reduces to the minimization of (14) ∑ J = 1 N   α J - α 0 · I     I n - A  ( α μ , β ; p , q , u , v , x , y ; t ) · I J )  2 2 .

[0079] Since the sensor signals are non-negative numbers, the normal distribution is a far from realistic model assumption which, however, has the practical advantage of basing the ML calibration on a (nonlinear) quadratic equalization problem—for example of the form (14)—which can be solved more efficiently than a general optimization problem of the form (9).

[0080] The conceptual and procedural simplicity of the ML calibration is achieved at the cost of complicated and possibly poorly conditioned maximization of the probability density (9), in which parameters are calculated from N·n scalar data k + 2  m - 1 + λ + N + ∑ J = 1 N   J  ;

[0081] k=6 or k=4, l:=number of parameters required for specifying the functions a:R→R₊ and d:R^(n)→R₊—in the case of (5), (10), (13) l=3. If each code line is to be produced as an image on M signal vectors on average N ≈ M · m 1 N · ∑ J = 1 N      J 

[0082] must be chosen. For the typical values m=1023, n=1024, 1 N · ∑ J = 1 N      J  ≈ n 20

[0083] and M≈12, the result is N≈240, so that about 15,000 parameters can be determined simultaneously from about 250,000 signal values via maximization of (9). Optimization problems of this order of magnitude can be efficiently solved only when their structural properties are used for reducing the complexity, and these must be “modelled into” the solution to the problem. Thus, in specifying the function a:R→R₊—as in example (5)—care should be taken to ensure a compact carrier so that the matrices occurring in (9) are sparse. Furthermore, boundary conditions such as I_(i) ^(J)≦0, α_(i) ⁻<α_(i) ⁺, etc. should be ignored; in the case of good data the ML estimated values should automatically fulfil these conditions, and any infringements indicate that the optimization of (9) has failed, or certain modelling formulations should be modified. If the normal distribution (10) is used in (9), it may be worthwhile eliminating the variables I^(J)εR|^(ℑ) ^(J) | and {overscore (w)}^(J)εR^(n) (12)—or α₀εR in (14) —by exactly solving the corresponding linear equalization problems: then, only the parameters β¹, . . . ,β^(N), α₀ ⁺,α₁ ^(μ), . . . , α_(m−1) ^(μ), p,q,u,v,x,y,t occur explicitly, but their functional logical combination is more complex (due to the occurrence of Moore-Penrose pseudoinverses).

[0084] The maximization of (9) is effected according to the prior art to date by a proven interative method; these are efficient when (9) can be continuously differentiated for all parameters to be estimated, and all partial first derivatives can be calculated analytically. Since the function Tη defined according to (0) can be continuously differentiated as often as desired for all its arguments, it is necessary to ensure continuous differentiability only in the choice of the functions a:R→R₊ and d:R^(n)→R₊; it is ensured in the case of the choices (5) and (10).

[0085] Iteration methods require starting values which, for reliability and efficiency of optimization, should be close to the optimum, i.e. in the present case ML estimated values. For the parameters α₀ ⁺, α₁ ^(μ), . . . ,α_(m−1) ^(μ) and p, q, u, v, x,y, the required values α̂_(i)^(u),

[0086] 0≦i <m, and {circumflex over (p)}, {circumflex over (q)}, û={circumflex over (v)}={circumflex over (x)}=ŷ=0 can be used, and starting values from separate signal analyses can be obtained for the parameters specifying a:R→R₊ and d: R^(n)→R₊. In the course of the decoding of the signal vector α^(J)εR^(n), i.e. the determination of the index quantity ℑ^(J)⊂[0,m[, 1≦J≦N, the maximum, the median or the centre of gravity ŝ_(J)^(i)∈] − 1, 1[

[0087] of the peak produced by the code line i and signal I(ŝ_(i)^(J))

[0088] is calculated approximately—e.g. by linear interpolation—for iεℑ^(J); in agreement with (1) and (4), β ^ J  : = 1  J   ∑ i    ∈     η 2 - 1  ( α ^ i - + α ^ i + 2 , s ^ i J ; p ^ , q ^ , u ^ , v ^ , x ^ , y ^ )     and   I ^ J  : = c · [ I  ( s ^ min  ( J ) J ) M I  ( s ^ max  ( J ) J ) ] ∈ R  J     ( 15 )

[0089] are chosen as starting values for β^(J) and I^(J)εR|^(ℑ) ^(J) |, where cεR₊ is the scaling factor determined by the specification of a:R→R₊. If the parameters I^(J)εR|^(ℑ) ^(J) | are eliminated as explained above, the calculation of their starting values is dispensed with.

[0090] Basing the calibration on a mathematical model—the formulation (7) with the functions η, a, d—has further advantages in addition to the procedural simplicity achieved thereby:

[0091] 1. Multiple sensor: The ML calibration can easily be extended to include angle sensors having a plurality of diode arrays: the product (9) to be maximized then also extends over all arrays, the parameters α₀ ⁺, α₁ ^(μ), . . . , α_(m−1) ^(μ) being common to all signal vectors and the circle position angles β̂¹,  …  , β̂^(N)

[0092] of two arrays differing from one another by a constant offset. The angle offsets relative to a selected array are also estimated, and the linear dependencies (obtained from the derivation of equation (0)) which exist between the parameters u,v,x,y of different arrays can be ignored or taken into account.

[0093] 2. Hybrid ML calibration: If reference angles β̂¹,  …  , β̂^(N),

[0094] 1≦N′≦N, are available for some of the circle position angles β¹, . . . ,β^(N′), for example β¹, . . . ,β^(N′), they can also be used for the ML calibration by joining the model ω J = β ^ J - J - 0 , 1 ≤ J ≤ N ′ , 0 ∈ R     an     offset , ( 16 )

[0095] to (8). As in (7), the vector ${\omega \text{:}} = {\begin{bmatrix} \omega^{1} \\ M \\ \omega^{N^{\prime}} \end{bmatrix}\quad \in R^{N^{\prime}}}$

[0096] is modelled as a random vector with probability density d′: R^(N′)→R₊, and the cumulative probability density d ′  ( [ β 1 ^ - β 1 - β 0 M β N ′ ^ - β N ′ - β 0 ] ) · ∏ J = 1 N     d  ( a J - A  ( α J μ , β J ; p , q , u , v , x , y ; t ) · I J ) ∈ R + ( 17 )

[0097] is maximized instead of (9). The extension of the hybrid ML calibration to include multiple sensors presents no problems.

[0098] 3. Residue analysis: After calibration is complete, the so-called residual vectors w ^ J  :  = ( 8 )  α J - A  ( α ^ μ  J  β ^ J ; p ^ , q ^ , u ^ , v ^ , x ^ , y ^ ; t ^ ) · I ^ J ∈ R n , I ≤ J ≤ N , ( 18 )

[0099] can be calculated, which, according to the model, should be realizations of independent d-distributed random vectors. By means of statistical test methods, it is possible to investigate the extent to which this applies, from which it is possible to estimate how realistic the model is and whether it should be modified.

[0100] 4. Angle estimation: The angle measurement with the calibrated angle sensor uses the same model as the ML calibration: Instead of (9), d ( a - A  ( α ^ μ , β ; p ^ , q ^ , u ^ , v ^ , x ^ , y ^ ; t ^ ) · I ∈ R + ( 19 )

[0101] is minimized, and the ML estimated value {circumflex over (β)} for β is output as an angle measurement; the ML estimated value Î for I is an irrelevant byproduct, as in the calibration. The calculation of the starting values for the iteration and the extension to include multiple sensors are the same as in the case of the ML calibration.

[0102] 5. Permanent self-calibration: The signal vector αεR^(n) in (19) contains sufficient information for also estimating some of the parameters p,q,u,v,x,y,t in addition to β and I. It would therefore be possible to realize permanent self-calibration—easily extendable to include multiple sensors—which would possibly reduce the temperature and ageing influences on the accuracy of measurement. The estimation of the parameters p,q,u,v,x,y,t would utilize the fact that—in contrast to β and I—a priori information is available in the form of the latest ML estimated values {circumflex over (p)},{circumflex over (q)},û,{circumflex over (v)},{circumflex over (x)},ŷ,{circumflex over (t)}.

[0103] Of course, the calibration described is only one of many possible embodiments and a person skilled in the art can derive alternative mathematical models or implementation forms, for example using differently formed partial systems, alternative codes or other means for imaging and detection or for signal processing. 

1. Method for calibrating a measuring instrument comprising at least two partial systems (K; M) movable relative to one another and comprising means (L) for producing an image of at least one first partial system (K) on at least one detecting component (A) of at least one of the partial systems (K; M), comprising the following steps, it being possible for one or more of the following steps to be carried out several times, formulation of a mathematical model describing the positions of the partial systems relative to one another and at least one image, derivation of at least one parameter set which quantifies the factors influencing systematic measurement errors of the measuring instrument, with at least one parameter from the mathematical model, generation of an image of structural elements (S) determining the relative position of a partial system and belonging to at least one first partial system (K)—preferably of a mark made on the first partial system (K)—on the second partial system (M) so that the image contains information about the relative position of the partial systems conversion of the image of the structural elements of the first partial system into signals by the detecting component (A), recording of at least one signal vector with at least one component, which vector contains information about the relative position of the partial systems (K; M), from the signals of the detecting component (A), estimation of values of the parameter set from the at least one signal vector, derivation and provision of correction values, which reduce systematic measurement errors of the measuring instrument, from the values of the parameter set.
 2. Method according to claim 1, characterized in that at least the position of the structural elements (S) on the first partial system (K) and a quantity which describes a movement of the partial systems relative to one another are quantified as factors influencing systematic measurement errors of the measuring instrument.
 3. Method according to claim 1 or 2, characterized in that the mark contains a code having the property that the code segment can be unambiguously localized on the first partial system (K) from the image of a code segment on the detecting component (A).
 4. Method according to claim 3, characterized in that the coding corresponds to a m-sequence, i.e. a sequence of length m=2^(l)−1, with l as a natural number and the sequence as a cyclic binary sequence b, consisting of $\frac{m - 1}{2}$

zeros and $\frac{m + 1}{2}$

ones, which has the property that, for each natural number n<m, there is exactly one partial sequence of b consisting of l successive digits which represents n in binary form.
 5. Method according to any of the preceding claims, characterized in that the parameter set contains at least one of the following parameters axis d (d) of rotation of the partial system, area K in which the mark of a partial system lies, at least one of the partial systems having a form which in particular is at least partly smooth and, for example, has one of the following shapes disc ring sphere cylinder, position of the mark for example an angle α (α) for describing the position of a code line if the mark consists of a code having code lines which are applied radially to a partial system having a partly flat surface, preferably disc-shaped, in the plane of this surface, for example as a sequence of alternately transparent and opaque code lines or as a sequence of code lines having alternately different reflectivity, position angle β (β) for describing the rotation of a partial system (K) about the axis d (d) of rotation relative to other partial systems, position parameter of a radiation source if the imaging means (L) include one or more sources of electromagnetic radiation, in particular light sources, position parameter of the point of intersection (D) of the axis d (d) of rotation with the area carrying the mark, position parameter of a diode array if the detecting component (A) includes one or more arrays of photosensitive diodes, in particular in linear arrangement.
 6. Method according to claim 5, characterized in that matching of the zero position of the position angle α (α) measured in plane K and of the position angle β (β) measured in the plane normal to the axis d (d) of rotation is effected so that, in the mathematical model describing the image, the position angle β (β) occurs only as a difference relative to the position angle α (α).
 7. Method according to any of the preceding claims, characterized in that the parameter set includes at least one, preferably dimensionless and/or time-invariant, parameter which represents a logical combination of parameters, in particular from the mathematical model.
 8. Method according to any of the preceding claims, characterized in that the method uses external reference quantities, which are not determined by the method, as values for one or more parameters.
 9. Method according to any of the preceding claims, characterized in that, before the conversion of the image of structural elements of the first partial system (K) into signals, the method includes, as an additional step, at least one further imaging of structural elements (S) of a partial system, which determine the relative position of a partial system, on at least one of the partial systems, in particular of the second partial system (M) on the first partial system (K), optionally a plurality of further images following one another in succession.
 10. Measuring instrument which can be calibrated by a method according to any of claims 1 to 9, comprising at least two partial systems (K; M) movable relative to one another and comprising means (L) for generating an image of at least one first partial system (K) on at least one detecting component (A) of at least one of the partial systems (K; M), characterized in that the means (L) for generating an image produce an image of structural elements which determine the relative position of a partial system—preferably of a mark made on the first partial system (K)—in such a way that the image contains information about the relative position of the partial systems, and that the following components are provided at least one detecting component (A) which converts the image of the structural elements (S) of the first partial system (K) into signals, means for recording at least one signal vector with at least one component, which contains information about the relative position of the partial systems (K; M), from the signals of the at least one detecting component (A), means for deriving and for providing correction values which reduce the systematic measurement errors of the measuring instrument and means for reducing the systematic measurement errors of the measuring instrument.
 11. Measuring instrument according to claim 10, characterized in that at least one of the partial systems (K; M) has a rotationally symmetrical form which, for example, has one of the following shapes, disc ring sphere cylinder.
 12. Measuring instrument according to either of claims 10 and 11, characterized in that the imaging means (L) include one or more sources of electromagnetic radiation, in particular light sources.
 13. Measuring instrument according to any of claims 10 to 12, characterized in that the mark includes a code having the property that the code segment can be unambiguously localized on the first partial system (K) from the image of a code segment on the detecting component (A).
 14. Measuring instrument according to claim 13, characterized in that the coding corresponds to a m-sequence, i.e. a sequence of the length m=2^(l)−1, with l as a natural number and the sequence as a cyclic binary sequence b, consisting of $\frac{m - 1}{2}$

zeros and $\frac{m + 1}{2}$

ones, which has the property that, for each natural number n<m, there is exactly one partial sequence of b consisting of l successive digits which represents n in binary form.
 15. Measuring instrument, in particular angle-measuring instrument, according to any of claims 10 to 14, characterized in that the mark consists of code lines which are applied radially on a partial system having a partly flat surface, preferably disc-shaped, in the plane of this surface, for example as a sequence of alternately transparent and opaque code lines, or as a sequence of code lines having alternately different reflectivity.
 16. Measuring instrument according to any of claims 10 to 15, characterized in that the detecting component (A) includes one or more arrays of photosensitive diodes, in particular in linear arrangement.
 17. Measuring instrument according to any of claims 10 to 16, characterized in that the means for deriving and for providing correction values and/or the means for reducing systematic measurement errors have an electronic circuit and/or an analogue and/or digital computer.
 18. Measuring instrument according to any of claims 10 to 17, characterized in that the means for reducing systematic measurement errors have an apparatus for hardware reduction of the systematic measurement errors, for example piezoelectric adjusting elements.
 19. Calibration device for carrying out the method according to any of claims 1 to 9 for a measuring instrument, which comprises at least two partial systems (K; M) movable relative to one another, characterized in that the following components are present means (L) for generating an image of at least one first partial system (K) of the measuring instrument on at least one detecting component (A) of the calibration device, which is mounted on at least one partial system of the measuring instrument, structural elements (S) determining the relative position of a partial system—preferably of a mark applied to the first partial system (K)—being imaged in such a way that the image contains information about the relative position of the partial systems (K; M), at least one detecting component (A) which converts the image of the structural elements (S) of the first partial system (K) into signals, means for recording at least one signal vector with at least one component, which contains information about the relative position of the partial systems (K; M), from the signals of the at least one detecting component (A), means for deriving and for providing correction values which reduce the systematic measurement errors of the measuring instrument and means for reducing systematic measurement errors of the measuring instrument.
 20. Calibration device according to claim 19, characterized in that at least one of the partial systems has a rotationally symmetrical form which has, for example, one of the following shapes disc ring sphere cylinder.
 21. Calibration device according to either of claims 19 and 20, characterized in that the means (L) for generating an image include one or more sources of electromagnetic radiation, in particular light sources.
 22. Calibration device according to any of claims 19 to 21, characterized in that the mark includes a code having the property that the code segment can be unambiguously localized on the partial system (K) from the image of a code segment on the detecting component (A).
 23. Calibration device according to claim 22, characterized in that the coding corresponds to a m-sequence, i.e. a sequence of the length m=2^(l)−1, with l as a natural number and the sequence as a cyclic binary sequence b, consisting of $\frac{m - 1}{2}$

zeros and $\frac{m + 1}{2}$

ones, which has the property that, for each natural number n<m, there is exactly one partial sequence of b consisting of l successive digits which represents n in binary form.
 24. Calibration device according to any of claims 19 to 23, characterized in that the mark consists of code lines which are applied radially on a partial system having a partly flat surface, preferably disc-shaped, in the plane of this surface, for example as a sequence of alternately transparent and opaque code lines, or as a sequence of code lines having alternately different reflectivity.
 25. Calibration device according to any of claims 19 to 24, characterized in that that the detecting component (A) includes one or more arrays of photosensitive diodes, in particular in linear arrangement.
 26. Calibration device according to any of claims 19 to 25, characterized in that the means for deriving and for providing correction values and/or the means for reducing systematic measurement errors have an electronic circuit and/or an analogue and/or digital computer.
 27. Calibration device according to any of claims 19 to 26, characterized in that that the means for reducing systematic measurement errors have an apparatus for hardware reduction of the systematic measurement errors, for example piezoelectric adjusting elements.
 28. Calibration device according to any of claims 19 to 27, characterized in that the calibration device and/or components of the calibration device are in modular form.
 29. Use of a method according to any of claims 1 to 9 for calibrating a plurality of measuring instruments, characterized in that at least one of the steps formulation of a mathematical model and derivation of a parameter set is carried out for at least two measuring instruments together.
 30. Computer program product comprising program code which is stored on a machine-readable medium, for carrying out at least one of the steps of the method according to any of claims 1 to 9, estimation of the values of the parameter set from the at least one signal vector, derivation and provision of correction values, which reduce systematic measurement errors of the measuring instrument, from the values of the parameter set, in particular if the program code is implemented in a computer.
 31. Analogue or digital computer data signal, embodied by an electromagnetic wave, having a program code segment for carrying out at least one of the steps of the method according to any of claims 1 to 9, estimation of the values of the parameter set from the at least one signal vector, derivation and provision of correction values, which reduce systematic measurement errors of the measuring instrument, from the values of the parameter set, in particular if the program code is implemented in a computer. 